LOAN DEFAULT ANALYSIS IN EUROPE: TRACKING REGIONAL VARIATIONS USING BIG DATA

An Academic presentation byDr. Nancy Agens, Head, Technical Operations, Phdassistance Group www.phdassistance.comEmail: info@phdassistance.com

Outline

TODAY'S DISCUSSION

In Brief

Background

Tests

European Perspect ive

Big Data

Conclus ion

In Brief

You will find the best dissertation research areas / topics for future researchers enrolled in Economics & Finance. In order to identify the future research topics, we have reviewed the Finance (recent peer-

reviewed studies) on Data Analysis. Multiple factors that affect the bank stability and ensure that proper documentation is maintained Main

objectives are to conduct stress tests on the banks.

Banks were never anticipated to fail especially the large banks like AIG.

Its collapse led to a complete failure of the insurance company and one of the main factors of the 2008 financial crises.

The main problem was that they had given out too many loans and guarantees to the borrowers even when they did not have enough capital in the reserves for the compensation.

The European Banking Authority (EBA) is responsible for the European banks and comes under the jurisdiction of the European Union (EU).

Its main objectives are to conduct stress tests on the banks in order to improve the transparencyin the financial system and identify the flaws and mismatches in capital and investments.

Background

Tests

The various tests and accounting models that are available are the Stress Tests, Credit Loss, etc.

Bank stress tests use simulation by examining the balance of the firms and analyse the financial stress that is available.

This will help in identifying capital, investment, liquidity, etc. of the project and analyse the available capital.

Current Expected Credit Loss (CECL) is a type of credit loss model that is used to analyse the exchange of capital and the losses arising from it.

Contd..

Before the financial crisis of 2008, a conventional method known as Allowance for Loan and Lease Losses (ALLL) were used, however, in this type of model it does not adjust the reserve levels as per the required conditions.

Instead, it depends on the losses that incur but not realized.

This means that it will not be certain when the cash flow will take place in the future.

This negative outlook of the credits was not considered during the financial crisis and the reserves were not adjusted for future expected losses.

Hence, the improved CECL approach identifies the credit loss by considering the factors previously avoided.

European Perspective

The credit systems vary a lot between these two regions since they have different market structure and economic conditions and since they have different regulatory authorities.

The major reason for having fewer studies for Europe is due to the unavailability of reliable and consistent data for most European countries.

A repository known as European Data warehouse (ED) contains partial data that can fill the gap to some extent, which gives the researchers different opportunities to explore the credit market in Europe.

The number of loan defaults do not remain constant and has constant variations among the corporate world.

The loan defaults rates of corporates globally is shown in figure 1.

Fig. 1 Annual Global Default Rates For CLOs and Corporate Issuers Source: Vazza et al.,(2020)

The presence of large amount of data in some countries brings in a dilemma on how to process the data since this brings about additional complexities to the analysis.

The ability of machine learning algorithms to predict the financial analysis makes them very much efficient for the regulatory bodies to monitor the finances.

CECL and stress tests can be performed using these algorithms to get efficient results.

The data must contain various parameters required for the analysis like Loan to Value (LTV), Debt Service Coverage Ratio (DSCR), etc. as the indicators of loan credits.

Big Data

Conclusion

The different type of analysis has been seen and discussed for European banks.

Analysing the CECL of the banks using machine learning techniques through big data will greatly avoid loan defaults.

This will avoid the failure of banks thereby avoidingeconomic collapse.

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